Jau-er Chen, Chien-Hsun Huang, Jia-Jyun Tien
arXiv 27 Sep 2019 · Econometrics · publishedEconometrics (2021) · 13 citations (OpenAlex)
arXiv:1909.12592 · PDF · DOI · OpenAlex · Extracted main text
In this study, we investigate estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression. Our proposed econometric procedure builds on the Neyman-type orthogonal moment conditions of a previous study Chernozhukov, Hansen and Wuthrich (2018) and is thus relatively insensitive to the estimation of the nuisance parameters. The Monte Carlo experiments show that the estimator copes well with high-dimensional controls. We also apply the procedure to empirically reinvestigate the quantile treatment effect of 401(k) participation on accumulated wealth.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C… (2018) Double/debiased machine learning for treatment and structural parameters | 1.000 | 5 | 3 | 100% |
| 2 | Chernozhukov, V.\ and C.\ Hansen (2008) Instrumental variable quantile regression: A robust inference approach | 0.811 | 4 | 2 | 100% |
| 3 | Belloni, A.\ and V.\ Chernozhukov (2011) $l_1$-penalized quantile regression in high-dimensional sparse models | 0.737 | 3 | 2 | 100% |
| 4 | Chen, J.-E.\ and C.-W. Hsiang (2019) Causal random forests model using instrumental variable quantile regression self | 0.737 | 3 | 2 | 100% |
| 5 | Chiou, Y.-Y., Chen, M.-Y., and J.-E.\ Chen (2018) Nonparametric regression with multiple thresholds: estimation and inference self | 0.737 | 3 | 2 | 100% |
| 6 | Chernozhukov, V.\ and C.\ Hansen (2005) An IV model of quantile treatment effects | 0.585 | 3 | 1 | 100% |
| 7 | Chernozhukov, V.\ and C.\ Hansen (2004) The effects of 401(k) participation on the wealth distribution: An instrumental quantile regression analysis | 0.511 | 2 | 1 | 100% |
| 8 | Chernozhukov, V., Hansen, C.\ and M.\ Spindler (2015) Valid post-selection and post-regularization inference: An elementary, general approach | 0.511 | 2 | 1 | 100% |
| 9 | Athey, S (2017) Beyond prediction: Using big data for policy problem | 0.405 | 1 | 1 | 100% |
| 10 | Athey, S., Tibshirani, J., and S.\ Wager (2019) Generalized random forests | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 19 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Robust Orthogonal Machine Learning of Treatment Effects | 0.405 | 1 | 1 |
| 2 | 2303.02784 | 0.405 | 1 | 1 |
| 3 | Estimating Causal Effects with Double Machine Learning - A Method Evaluation | 0.000 | 1 | 1 |